PROJECT SUMMARY The studies proposed here represent a continuation of our molecular and clinical phenotyping work in a large and diverse interstitial cystitis/bladder pain syndrome (IC/BPS) patient population. We present preliminary data suggesting that IC/BPS comprises at least two distinct phenotypic subpopulations; one characterized as a bladder-centric disease process and the other characterized as a systemic pain syndrome. The significant variability in symptoms and associated syndromes (i.e. disease heterogeneity as well as disease severity) among IC/BPS patients contributes to the difficulty in developing targeted therapeutics. Therefore, we propose to leverage a large repository of retrospectively and prospectively collected unique clinical specimens (bladder biopsy tissue and peripheral blood from >400 IC/BPS patients and >100 non-IC controls) to identify molecular mechanisms that define patient subgroups. This will be accomplished by using a sophisticated molecular profiling scheme (weighted gene co-expression analysis network, WGCNA) to correlate the transcriptome with individual patient clinical data as an unbiased means to stratify patients into clinically relevant subgroups for which potential therapeutic targets will have been identified in the process. A second objective of these studies is, through gene expression profiling of longitudinally collected patient samples (bladder mucosal biopsies) compared with change/progression in patient clinical characteristics (e.g. duration of symptoms, pain and symptom scores on validated questionnaires, anesthetic bladder capacity), to identify molecular targets and biological pathways that are suggestive of disease progression in IC/BPS. To accomplish these objectives we propose three Specific Aims. Aim 1: Identification of molecular signatures that correlate with disease progression in IC/BPS. Analysis of tissues from 300 patients will provide a representative cross-section of the extensive phenotypic variability seen in IC/BPS. The use of unbiased weighted gene co-expression network analysis (WGCNA) will allow us to identify gene expression signatures that correlate with clinical features associated with disease progression. Aim 2: Identification of a blood-based molecular signature indicative of disease progression in IC/BPS. In this Aim we will evaluate blood collected from the same 300 IC/BPS patients (and 100 controls) in SA1 to identify a blood- based molecular signature that correlates with disease severity and is indicative of disease progression. We will also determine whether blood molecular signatures correlate with bladder mucosal molecular signatures, which may provide higher resolution phenotypic classification than current clinical measures. Aim 3: Identification of molecular signatures that predict disease progression in IC/BPS. We hypothesize that gene expression in the bladder mucosa of IC/BPS patients reflects disease status and that, over time, changes within an individual may indicate disease progression. We will identify groups of genes that predict disease progression using unbiased WGCNA of RNA-Seq data from longitudinal patient samples.